A probabilistic method for rigid-body motion planning using sampling from the medial axis of the free space

Abstract

Motion planning in the presence of obstacles is an important problem in robotics with numerous applications in other areas. While complete motion planning algorithms do exist, they are rarely used in practice since they are computationally infeasible in all but the simplest cases. For this reason, many recent efforts have focused on probabilistic methods, which sacrifice completeness in favor of computational feasibility and applicability. In particular, several algorithms, known as probabilistic roadmap planners, have been shown to perform well in a number of practical situations; however, their performance degrades when paths are required to pass through narrow passages in the free space. In this dissertation we p...